This section shows the main steps that have been applied to pre-process the raw data.
The CDOM spectra were modelled according to the information in Babin (2003).
acdom spectra were re-fitted using the complete data (i.e. between 350-500 nm) because the data in all_abs_transpose.txt started at 380 nm.Average background values calculated between 683-687 nm and subtracted from each spectrum.
Some files were in binary format, so I could not open them (ex.: C2001000.YSA).
Some spectra start at 300 nm while others at 350 nm.
Calculated the correlation between the measured and the fitted values.
Absorption spectra with any negative values below 500 nm were removed.
Exported the complete spectra (350-700 nm): both the raw and the modelled data.
Ed, Eu, Kd, Ku). I have cleaned the data by setting these negative values to NA.This graph shows the number of negative values for Ed by wavelength.
Eu is in fact Eu0- that was estimated using a two-exponential function model.
Ed is in fact Ed0- calculated from 0.94 x ed0+.
NA.Negative values in a, c, bp, a_dissolved and c_dissolved have been set to NA.
a(715) was used as the baseline, that is why the values are always at 0 (see next graph).
DOC, AQY) from Massimo 2000.Just some graphs to visualize the data. Note that the same colour palette will be used to represent the areas in all graphics.
There is a total of 424 different stations were sampled during the COASTLOOC expeditions.
C2001000, C2002000.We could also present the data in relation with its distance to the land to get an overview of the landscape on optical quantities. For instance, acdom will likely be higher for stations located close to the shore because of the terrestrial influence.
This graph shows an overview of the available variables (excluding radiometric measurements).
Overview of the averaged absorption spectra for each area.
Comparing acdom443 for the different areas shows that there is a clear open to coastal gradient.
We can see that the DOC follows the same pattern as acdom443.
We can also use scatter plots to further explore the relationships among variables.
Relationships between some pigments.
We could also assess the goodness of the relationships between total chlorophyll-a and phytoplankton absorption for each region.
In this section I am using the same three stations as in Oubelkheir et al. (2007) to explore the additive contributions of each type of absorption.
C6024000, a_p is lower than a_cdom around 400 nm and 550 nm. Should we use this to filter out problematic spectra?In this section, we will explore various diagnostic graphics to validate the data.
In theory, the dissolved absorption measured by the spectrofluorometer aCDOM should be comparable with the dissolved absorption measured by the AC9 (using a filter). The next graphs compare both measurements at common wavelengths for the different areas.
We can also use sina plot as another way to compare the same data.
The data is a mix of temporal and spatial observations, so how should we present the data?
By area?
No absorption for Med. Sea (Case 1). Is it normal?
There are a lot of nutrient parameters that have values of zero. Are they true zero or they indicate missing values?
There are wavelength gaps in the AC9, irradiance and reflectance data. Is that normal?
Calculate s_nap and s_cdom. See the method in Babin where he removes some wavelengths to calculate s_nap.
test